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<h1><a name="SECTION0027600000000000000000"></a><a name="profile-calibration"></a>
<br>
25.6 Calibration 
</h1>

<p>
The profiler of the <tt class="module">profile</tt> module subtracts a constant from each
event handling time to compensate for the overhead of calling the time
function, and socking away the results.  By default, the constant is 0.
The following procedure can
be used to obtain a better constant for a given platform (see discussion
in section Limitations above).

<p>
<div class="verbatim"><pre>
import profile
pr = profile.Profile()
for i in range(5):
    print pr.calibrate(10000)
</pre></div>

<p>
The method executes the number of Python calls given by the argument,
directly and again under the profiler, measuring the time for both.
It then computes the hidden overhead per profiler event, and returns
that as a float.  For example, on an 800 MHz Pentium running
Windows 2000, and using Python's time.clock() as the timer,
the magical number is about 12.5e-6.

<p>
The object of this exercise is to get a fairly consistent result.
If your computer is <em>very</em> fast, or your timer function has poor
resolution, you might have to pass 100000, or even 1000000, to get
consistent results.

<p>
When you have a consistent answer,
there are three ways you can use it:<a name="tex2html189"
  href="#foot75790"><sup>25.2</sup></a>
<p>
<div class="verbatim"><pre>
import profile

# 1. Apply computed bias to all Profile instances created hereafter.
profile.Profile.bias = your_computed_bias

# 2. Apply computed bias to a specific Profile instance.
pr = profile.Profile()
pr.bias = your_computed_bias

# 3. Specify computed bias in instance constructor.
pr = profile.Profile(bias=your_computed_bias)
</pre></div>

<p>
If you have a choice, you are better off choosing a smaller constant, and
then your results will ``less often'' show up as negative in profile
statistics.

<p>
<br><hr><h4>Footnotes</h4>
<dl>
<dt><a name="foot75790">... it:</a><A
 href="profile-calibration.html#tex2html189"><sup>25.2</sup></a></dt>
<dd>Prior to Python 2.2, it
  was necessary to edit the profiler source code to embed the bias as
  a literal number.  You still can, but that method is no longer
  described, because no longer needed.

</dd>
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<span class="release-info">Release 2.5.1, documentation updated on 18th April, 2007.</span>
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